Abstract / summary Lung cancer is the leading cause of cancer death in the US and worldwide, largely because most patients have advanced, incurable disease at the time of diagnosis. However, lung cancer screening (LCS) with low-dose computed tomography (LDCT) has the potential to revolutionize lung cancer outcomes through early detection. As LCS is disseminated into real-world settings and populations, a key outstanding question is whether the benefits/harms ratio found in clinical trials will apply to an older and sicker population. The basic conundrum facing LCS candidates is that the single risk factor most strongly linked to lung cancer -- smoking history -- is also strongly linked to morbidity and death from non-lung cancer causes (e.g. chronic obstructive pulmonary disease emphysema), which limit life expectancy and increase risk of complications from diagnostic or therapeutic procedures. The overarching goal of our proposed study is to precisely characterize this vulnerable subpopulation with high comorbidity burden, quantifying for them the benefits and harms of LCS to enable more informed decision-making by patients contemplating LCS. Our study will help close this knowledge gap by leveraging real-world data to more fully characterize this subpopulation of ?marginal? LCS candidates, reducing the uncertainty currently facing patients and providers. More specifically, we propose to leverage electronic health records and claims data for patients ages 55-80 (n~34,039) undergoing annual screening with LDCT in geographically diverse real-world settings from 2016-2022. We will then use these observational data with validated models in the Cancer Intervention Simulation Network to simulate LCS outcomes in the real-world US population. By generating previously unavailable real-world data for use in validated simulation models, this proposal responds directly to calls to improve patient- centered decision-making in LCS candidates for whom the net benefits of screening are currently highly uncertain.